COBALT embeds catalogs into anchored discrete latent graphs, applies random tree decomposition and additive SAAS-GP surrogates to heteroscedastic MC-FEA data, and performs discrete trust-region acquisition to optimize high-dimensional categorical structural designs under aleatoric uncertainty while
Title resolution pending
1 Pith paper cite this work, alongside 3 external citations. Polarity classification is still indexing.
1
Pith paper citing it
3
external citations · external index
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Categorical Optimization with Bayesian Anchored Latent Trust Regions for Structural Design under High-Dimensional Uncertainty
COBALT embeds catalogs into anchored discrete latent graphs, applies random tree decomposition and additive SAAS-GP surrogates to heteroscedastic MC-FEA data, and performs discrete trust-region acquisition to optimize high-dimensional categorical structural designs under aleatoric uncertainty while